Statistical tools

Reconstruct IPD from a published Kaplan–Meier curve — then extrapolate

Trace a published survival figure, recover the patient-level data (Guyot reconstruction), fit the standard parametric and flexible-parametric models, and extrapolate to a lifetime horizon. The lesson: curves that agree inside the trial fan out once the data run out — and the lifetime mean is governed by the model, not the data.

Load the worked example (a real breast-cancer trial) or trace a curve, then reconstruct to fit and extrapolate. The candidate models, AIC/BIC ranking and lifetime estimates appear here.

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Survival extrapolation for HTA

Choosing and justifying an extrapolation model — parametric, flexible-parametric, spline, or with external evidence and general-population mortality — is a core part of a cost-effectiveness submission. It is what merlin and our HTA work are for.

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Reconstruction follows Guyot et al. (2012), validated against IPDfromKM; fits are by in-browser maximum likelihood, validated against merlin (and cross-checked against flexsurv) on the German Breast Cancer Study data that ships the worked example. Reconstructed data are pseudo-IPD — for teaching and exploratory analysis; validate any submission in merlin.